How Data Analytics Drive Sharing Economy Business Models?

Abstract

Several studies and reports published by Mckinsey, Gartner, Cesco, PwC, etc., confirm that data analytics offers companies more value and allows them to the creation of new and innovative ideas. This is why the data-driven approach has been the subject of considerable publicity in recent years. This approach has given rise to the emergence of many business models, all of which have created their own way of doing things. This is the case of many emergent business models who have noticed that several assets (goods or services) are not exploited effectively by the parties that hold them. We buy many products that we use only for a certain period and then put them aside. What if we could find one or more people who might need it? This is the question that these innovative business models had taken into account. They could see potential monetary benefits in these different resources, simply by facilitating their sharing. Some succeed by bursting the value chains and shaking up the established players: Uber for taxis, BlaBlaCar for interurban carpooling, Airbnb for accommodation, etc., and this is, of course, just the beginning, because the trend is accelerating. These are fascinating ideas that have led to the emergence of the sharing economy. But, one thing is clear, the ideas created by Uber, Airbnb, BlaBlaCar, etc. cannot be realized without determining what allows their development (how?) and, of course, the target (for whom?). These companies use the data to determine what to develop and target, to create untapped sharing market opportunities. Many researchers have found the potential of large amounts of data produced and collected by many sharing platforms. The analysis of these quantities not only helps to promote the performance of these models or operationalize their activities, but also to predict economic results such as inflation, unemployment, housing prices, etc. All sharing platforms and applications rely on data and analysis to develop practices and determine who to target. These data are increasingly used today because of the conjunction of a number of factors, such as: • The constant decrease in data storage costs; • The increase of the computing power; • The production of large amounts of data, which is largely unstructured and requires different operating techniques and which cannot be preceded by traditional methods. Being able to generate value, in the context of the sharing economy, and make big data more profitable is based on the ability of companies to analyze the amount available data. The challenge, therefore, lie in the ability to extract value from the amount volume of data produced in real-time continuous streams with multiple form and from multiple sources. In another word, the key to explore data and uncover secrets from it is to find and develop applicable ways in such a way 459 https://iises.net/proceedings/international-academic-conference-barcelona/front-page 23 September 2019, IISES International Academic Conference, Barcelona IISES to extract knowledge that can conduct any bu...

Sharing Economy Development: Empirical Analysis of Technological Factors

“…Uber, Lyft, Lya, Breeze, JustPark, Wingz, BlaBlaCar, Airbnb, HomeAway, WeWork, Gumtree, e-Bay, Etsy, Craiglist, UpWork, Amazon, TaskRabbit, Freelancer, Mechanicalturk, Fiverr, Kickstarter, Vinted [22,58,59,[65][66][67][68][69][70][71][72] Internet platforms Online platforms like Airbnb, Uber, Lyft, etc., allow users to share their assets or transportation services with others. These platforms are becoming an important alternative to traditional service companies, allowing people to generate income from their assets and services.…”

“…Zhu et al (2017) examined users' motivations for sharing apps and platforms and found what drives users to use one of the new mobile phone-sharing economies: travel-sharing programs. Sedkaoui and Benaichouba (2019) analyze how SE companies like Uber, Airbnb, BlaBlaCar, etc., use data and advanced analytics to strengthen their business models using sharing platforms and apps [58]. Different authors such as Anwar (2018), Sedkaoui and Benaichouba (2019), Wu and Yang (2021), Wirtz (2021), and Garud et al (2022) analyze different SE companies based on digital platforms, examine the key concepts of analytics and big data, their challenges, importance, and the role they play in creating new opportunities for SE companies, and they also analyze how companies with SE economic business models use data analysis to create value [22,58,[66][67][68].…”

“…Many authors examine the services provided on different internet platforms such as Uber, Airbnb, Gumtree, eBay, Amazon, and others [58][59][60]68,70,71,[73][74][75][76][77]. Lingaitien ė et al (2022) examined customer habits using digital sharing platforms when they need to share products and services that help save natural resources and support sustainable development [60].…”